Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.

• Tác giả: Tae Su Choi, Hugh I Kim, Min Kyung Kim, Soohyeong Kim, Wonseok Lee
• Ngôn ngữ: eng
• Ký hiệu phân loại: 620.004 Design, testing, measurement, quality, maintenance, repair
• Thông tin xuất bản: United States : Molecules and cells , 2025
• Bộ sưu tập: NCBI
• ID: 737986

Tóm tắt

Metal coordination is essential for structural/catalytic functions of metalloproteins that mediate a wide range of biological processes in living organisms. Advances in bioinformatics have significantly enhanced our understanding of metal-binding sites and their functional roles in metalloproteins. State-of-the-art computational models developed for metal-binding sites seamlessly integrate protein sequence and structural data to unravel the complexities of metal coordination environments. Our goal in this mini-review is to give an overview of these tools and highlight the current challenges (predicting dynamic metal-binding sites, determining functional metalation states, and designing intricate coordination networks) remaining in the predictive models of metal-binding sites. Addressing these challenges will not only deepen our knowledge of natural metalloproteins but also accelerate the development of artificial metalloproteins with novel and precisely engineered functionalities.